Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of different languages in a shared vector space. The common practice is to fine-tune the dense retriever via contrastive learning, whose effectiveness highly relies on the quality of the negative sample and the efficacy of mini-batch data. Different from the existing studies that focus on developing sophisticated model architecture, we propose a method to boost data utilization for multilingual dense retrieval by obtaining high-quality hard negative samples and effective mini-batch data. The extensive experimental results on a multilingual retrieval benchmark, MIRACL, with 16 languages demonstrate the effectiveness of our method by outperforming several existing strong baselines.
@article{arxiv.2509.09459,
title = {Boosting Data Utilization for Multilingual Dense Retrieval},
author = {Chao Huang and Fengran Mo and Yufeng Chen and Changhao Guan and Zhenrui Yue and Xinyu Wang and Jinan Xu and Kaiyu Huang},
journal= {arXiv preprint arXiv:2509.09459},
year = {2025}
}